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Song J, Liu Y, Guo R, Pacheco A, Muñoz-Zavala C, Song W, Wang H, Cao S, Hu G, Zheng H, Dhliwayo T, San Vicente F, Prasanna BM, Wang C, Zhang X. Exploiting genomic tools for genetic dissection and improving the resistance to Fusarium stalk rot in tropical maize. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:109. [PMID: 38649662 DOI: 10.1007/s00122-024-04597-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 03/07/2024] [Indexed: 04/25/2024]
Abstract
KEY MESSAGE A stable genomic region conferring FSR resistance at ~250 Mb on chromosome 1 was identified by GWAS. Genomic prediction has the potential to improve FSR resistance. Fusarium stalk rot (FSR) is a global destructive disease in maize; the efficiency of phenotypic selection for improving FSR resistance was low. Novel genomic tools of genome-wide association study (GWAS) and genomic prediction (GP) provide an opportunity for genetic dissection and improving FSR resistance. In this study, GWAS and GP analyses were performed on 562 tropical maize inbred lines consisting of two populations. In total, 15 SNPs significantly associated with FSR resistance were identified across two populations and the combinedPOP consisting of all 562 inbred lines, with the P-values ranging from 1.99 × 10-7 to 8.27 × 10-13, and the phenotypic variance explained (PVE) values ranging from 0.94 to 8.30%. The genetic effects of the 15 favorable alleles ranged from -4.29 to -14.21% of the FSR severity. One stable genomic region at ~ 250 Mb on chromosome 1 was detected across all populations, and the PVE values of the SNPs detected in this region ranged from 2.16 to 5.18%. Prediction accuracies of FSR severity estimated with the genome-wide SNPs were moderate and ranged from 0.29 to 0.51. By incorporating genotype-by-environment interaction, prediction accuracies were improved between 0.36 and 0.55 in different breeding scenarios. Considering both the genome coverage and the threshold of the P-value of SNPs to select a subset of molecular markers further improved the prediction accuracies. These findings extend the knowledge of exploiting genomic tools for genetic dissection and improving FSR resistance in tropical maize.
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Affiliation(s)
- Junqiao Song
- Henan University of Science and Technology, Luoyang, 471000, Henan, China
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- Anyang Academy of Agricultural Sciences, Anyang, 455000, Henan, China
| | - Yubo Liu
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- CIMMYT-China Specialty Maize Research Center, Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, 200063, China
| | - Rui Guo
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, 050035, Hebei, China
| | - Angela Pacheco
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
| | - Carlos Muñoz-Zavala
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
| | - Wei Song
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, 050035, Hebei, China
| | - Hui Wang
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- CIMMYT-China Specialty Maize Research Center, Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, 200063, China
| | - Shiliang Cao
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- Institute of Maize Research, Heilongjiang Academy of Agricultural Sciences, Harbin, 150070, Heilongjiang, China
| | - Guanghui Hu
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- Institute of Maize Research, Heilongjiang Academy of Agricultural Sciences, Harbin, 150070, Heilongjiang, China
| | - Hongjian Zheng
- CIMMYT-China Specialty Maize Research Center, Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, 200063, China
| | - Thanda Dhliwayo
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
| | - Felix San Vicente
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
| | - Boddupalli M Prasanna
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, Nairobi, 00621, Kenya
| | - Chunping Wang
- Henan University of Science and Technology, Luoyang, 471000, Henan, China.
| | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico.
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), CIMMYT-China Office, 12 Zhongguancun South Street, Beijing, 100081, China.
- Nanfan Research Institute, CAAS, Sanya, 572024, Hainan, China.
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Alemu A, Åstrand J, Montesinos-López OA, Isidro Y Sánchez J, Fernández-Gónzalez J, Tadesse W, Vetukuri RR, Carlsson AS, Ceplitis A, Crossa J, Ortiz R, Chawade A. Genomic selection in plant breeding: Key factors shaping two decades of progress. MOLECULAR PLANT 2024; 17:552-578. [PMID: 38475993 DOI: 10.1016/j.molp.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 01/22/2024] [Accepted: 03/08/2024] [Indexed: 03/14/2024]
Abstract
Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.
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Affiliation(s)
- Admas Alemu
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Johanna Åstrand
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden; Lantmännen Lantbruk, Svalöv, Sweden
| | | | - Julio Isidro Y Sánchez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Javier Fernández-Gónzalez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Wuletaw Tadesse
- International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco
| | - Ramesh R Vetukuri
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Anders S Carlsson
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera México-Veracruz, Texcoco, México 52640, Mexico
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
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Meher PK, Gupta A, Rustgi S, Mir RR, Kumar A, Kumar J, Balyan HS, Gupta PK. Evaluation of eight Bayesian genomic prediction models for three micronutrient traits in bread wheat (Triticum aestivum L.). THE PLANT GENOME 2023; 16:e20332. [PMID: 37122189 DOI: 10.1002/tpg2.20332] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 02/21/2023] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
In wheat, genomic prediction accuracy (GPA) was assessed for three micronutrient traits (grain iron, grain zinc, and β-carotenoid concentrations) using eight Bayesian regression models. For this purpose, data on 246 accessions, each genotyped with 17,937 DArT markers, were utilized. The phenotypic data on traits were available for 2013-2014 from Powerkheda (Madhya Pradesh) and for 2014-2015 from Meerut (Uttar Pradesh), India. The accuracy of the models was measured in terms of reliability, which was computed following a repeated cross-validation approach. The predictions were obtained independently for each of the two environments after adjusting for the local effects and across environments after adjusting for the environmental effects. The Bayes ridge regression (BayesRR) model outperformed the other seven models, whereas BayesLASSO (BayesL) was the least efficient. The GPA increased with an increase in the size of the training set as well as with an increase in marker density. The GPA values differed for the three traits and were higher for the best linear unbiased estimate (BLUE) (obtained after adjusting for the environmental effects) relative to those for the two environments. The GPA also remained unaffected after accounting for the population structure. The results of the present study suggest that only the best model should be used for the estimations of genomic estimated breeding values (GEBVs) before their use for genomic selection to improve the grain micronutrient contents.
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Affiliation(s)
- Prabina Kumar Meher
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Ajit Gupta
- Division of Statistical Genetics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Sachin Rustgi
- Department of Plant and Environmental Sciences, Pee Dee Research and Education Centre, Clemson University, Florence, South Carolina, USA
| | - Reyazul Rouf Mir
- Division of Genetics and Plant Breeding, SKUAST-Kashmir, Kashmir, India
| | - Anuj Kumar
- Department of Microbiology and Immunology, Dalhousie University, Halifax, Nova Scotia, Canada
- Laboratory of Immunity, Shantou University Medical College, Shantou, People's Republic of China
| | - Jitendra Kumar
- National Agri-Food Biotechnology Institute (NABI), Ajitgarh, India
| | - Harindra Singh Balyan
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
| | - Pushpendra Kumar Gupta
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, India
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Tadesse W, Gataa ZE, Rachdad FE, Baouchi AE, Kehel Z, Alemu A. Single- and multi-trait genomic prediction and genome-wide association analysis of grain yield and micronutrient-related traits in ICARDA wheat under drought environment. Mol Genet Genomics 2023; 298:1515-1526. [PMID: 37851098 PMCID: PMC10657311 DOI: 10.1007/s00438-023-02074-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 09/28/2023] [Indexed: 10/19/2023]
Abstract
Globally, over 2 billion people suffer from malnutrition due to inadequate intake of micronutrients. Genomic-assisted breeding is identified as a valuable method to facilitate developing new improved plant varieties targeting grain yield and micronutrient-related traits. In this study, a genome-wide association study (GWAS) and single- and multi-trait-based genomic prediction (GP) analysis was conducted using a set of 252 elite wheat genotypes from the International Center for Agricultural Research in Dry Areas (ICARDA). The objective was to identify linked SNP markers, putative candidate genes and to evaluate the genomic estimated breeding values (GEBVs) of grain yield and micronutrient-related traits.. For this purpose, a field trial was conducted at a drought-prone station, Merchouch, Morocco for 2 consecutive years (2018 and 2019) followed by GWAS and genomic prediction analysis with 10,173 quality SNP markers. The studied genotypes exhibited a significant genotypic variation in grain yield and micronutrient-related traits. The GWAS analysis identified highly significantly associated markers and linked putative genes on chromosomes 1B and 2B for zinc (Zn) and iron (Fe) contents, respectively. The genomic predictive ability of selenium (Se) and Fe traits with the multi-trait-based GP GBLUP model was 0.161 and 0.259 improving by 6.62 and 4.44%, respectively, compared to the corresponding single-trait-based models. The identified significantly linked SNP markers, associated putative genes, and developed GP models could potentially facilitate breeding programs targeting to improve the overall genetic gain of wheat breeding for grain yield and biofortification of micronutrients via marker-assisted (MAS) and genomic selection (GS) methods.
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Affiliation(s)
- Wuletaw Tadesse
- The International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco
| | - Zakaria El Gataa
- The International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco
| | - Fatima Ezzahra Rachdad
- The International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco
| | - Adil El Baouchi
- AgroBioSciences, Mohammed VI Polytechnic University (UM6P), Ben Guerir, Morocco
| | - Zakaria Kehel
- The International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco
| | - Admas Alemu
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
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5
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Liu H, Yu S. A dimensionality-reduction genomic prediction method without direct inverse of the genomic relationship matrix for large genomic data. PLANT CELL REPORTS 2023; 42:1825-1832. [PMID: 37750948 DOI: 10.1007/s00299-023-03069-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 09/08/2023] [Indexed: 09/27/2023]
Abstract
KEY MESSAGE A new genomic prediction method (RHPP) was developed via combining randomized Haseman-Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm. Computational efficiency is becoming a hot issue in the practical application of genomic prediction due to the large number of data generated by the high-throughput genotyping technology. In this study, we developed a fast genomic prediction method RHPP via combining randomized Haseman-Elston regression (RHE-reg), PCR based on genomic information of core population, and preconditioned conjugate gradient (PCG) algorithm. The simulation results demonstrated similar prediction accuracy between RHPP and GBLUP, and significantly higher computational efficiency of the former with the increase of individuals. The results of real datasets of both bread wheat and loblolly pine demonstrated that RHPP had a similar or better predictive accuracy in most cases compared with GBLUP. In the future, RHPP may be an attractive choice for analyzing large-scale and high-dimensional data.
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Affiliation(s)
- Hailan Liu
- Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, Sichuan, China.
| | - Shizhou Yu
- Molecular Genetics Key Laboratory of China Tobacco, Guizhou Academy of Tobacco Science, Guiyang, 550081, Guizhou, China.
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6
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Roy C, Kumar S, Ranjan RD, Kumhar SR, Govindan V. Genomic approaches for improving grain zinc and iron content in wheat. Front Genet 2022; 13:1045955. [PMID: 36437911 PMCID: PMC9683485 DOI: 10.3389/fgene.2022.1045955] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 10/24/2022] [Indexed: 09/29/2023] Open
Abstract
More than three billion people worldwide suffer from iron deficiency associated anemia and an equal number people suffer from zinc deficiency. These conditions are more prevalent in Sub-Saharan Africa and South Asia. In developing countries, children under the age of five with stunted growth and pregnant or lactating women were found to be at high risk of zinc and iron deficiencies. Biofortification, defined as breeding to develop varieties of staple food crops whose grain contains higher levels of micronutrients such as iron and zinc, are one of the most promising, cost-effective and sustainable ways to improve the health in resource-poor households, particularly in rural areas where families consume some part of what they grow. Biofortification through conventional breeding in wheat, particularly for grain zinc and iron, have made significant contributions, transferring important genes and quantitative trait loci (QTLs) from wild and related species into cultivated wheat. Nonetheless, the quantitative, genetically complex nature of iron and zinc levels in wheat grain limits progress through conventional breeding, making it difficult to attain genetic gain both for yield and grain mineral concentrations. Wheat biofortification can be achieved by enhancing mineral uptake, source-to-sink translocation of minerals and their deposition into grains, and the bioavailability of the minerals. A number of QTLs with major and minor effects for those traits have been detected in wheat; introducing the most effective into breeding lines will increase grain zinc and iron concentrations. New approaches to achieve this include marker assisted selection and genomic selection. Faster breeding approaches need to be combined to simultaneously increase grain mineral content and yield in wheat breeding lines.
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Affiliation(s)
- Chandan Roy
- Department of Genetics and Plant Breeding, Agriculture University, Jodhpur, Rajasthan, India
| | - Sudhir Kumar
- Department of Plant Breeding and Genetics, Bihar Agricultural University, Bhagalpur, Bihar, India
| | - Rakesh Deo Ranjan
- Department of Plant Breeding and Genetics, Bihar Agricultural University, Bhagalpur, Bihar, India
| | - Sita Ram Kumhar
- Department of Genetics and Plant Breeding, Agriculture University, Jodhpur, Rajasthan, India
| | - Velu Govindan
- International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico
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Rakotondramanana M, Tanaka R, Pariasca-Tanaka J, Stangoulis J, Grenier C, Wissuwa M. Genomic prediction of zinc-biofortification potential in rice gene bank accessions. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:2265-2278. [PMID: 35618915 PMCID: PMC9271118 DOI: 10.1007/s00122-022-04110-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 04/19/2022] [Indexed: 06/15/2023]
Abstract
A genomic prediction model successfully predicted grain Zn concentrations in 3000 gene bank accessions and this was verified experimentally with selected potential donors having high on-farm grain-Zn in Madagascar. Increasing zinc (Zn) concentrations in edible parts of food crops, an approach termed Zn-biofortification, is a global breeding objective to alleviate micro-nutrient malnutrition. In particular, infants in countries like Madagascar are at risk of Zn deficiency because their dominant food source, rice, contains insufficient Zn. Biofortified rice varieties with increased grain Zn concentrations would offer a solution and our objective is to explore the genotypic variation present among rice gene bank accessions and to possibly identify underlying genetic factors through genomic prediction and genome-wide association studies (GWAS). A training set of 253 rice accessions was grown at two field sites in Madagascar to determine grain Zn concentrations and grain yield. A multi-locus GWAS analysis identified eight loci. Among these, QTN_11.3 had the largest effect and a rare allele increased grain Zn concentrations by 15%. A genomic prediction model was developed from the above training set to predict Zn concentrations of 3000 sequenced rice accessions. Predicted concentrations ranged from 17.1 to 40.2 ppm with a prediction accuracy of 0.51. An independent confirmation with 61 gene bank seed samples provided high correlations (r = 0.74) between measured and predicted values. Accessions from the aus sub-species had the highest predicted grain Zn concentrations and these were confirmed in additional field experiments, with one potential donor having more than twice the grain Zn compared to a local check variety. We conclude utilizing donors from the aus sub-species and employing genomic selection during the breeding process is the most promising approach to raise grain Zn concentrations in rice.
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Affiliation(s)
- Mbolatantely Rakotondramanana
- Rice Research Department, The National Center for Applied Research on Rural Development (FOFIFA), 101, Antananarivo, Madagascar
| | - Ryokei Tanaka
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo, 113-8657, Japan
| | - Juan Pariasca-Tanaka
- Crop, Livestock and Environment Division, Japan International Research Center for Agricultural Sciences (JIRCAS), 1-1 Ohwashi, Tsukuba, Ibaraki, 305-8686, Japan
| | - James Stangoulis
- College of Science and Engineering, Flinders University, Bedford Park, SA, 5042, Australia
| | - Cécile Grenier
- CIRAD, INRAE, Institut Agro, UMR AGAP Institut, Univ Montpellier, 34398, Montpellier, France
| | - Matthias Wissuwa
- Crop, Livestock and Environment Division, Japan International Research Center for Agricultural Sciences (JIRCAS), 1-1 Ohwashi, Tsukuba, Ibaraki, 305-8686, Japan.
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Baertschi C, Cao TV, Bartholomé J, Ospina Y, Quintero C, Frouin J, Bouvet JM, Grenier C. Impact of early genomic prediction for recurrent selection in an upland rice synthetic population. G3 (BETHESDA, MD.) 2021; 11:jkab320. [PMID: 34498036 PMCID: PMC8664429 DOI: 10.1093/g3journal/jkab320] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 08/16/2021] [Indexed: 11/14/2022]
Abstract
Population breeding through recurrent selection is based on the repetition of evaluation and recombination among best-selected individuals. In this type of breeding strategy, early evaluation of selection candidates combined with genomic prediction could substantially shorten the breeding cycle length, thus increasing the rate of genetic gain. The objective of this study was to optimize early genomic prediction in an upland rice (Oryza sativa L.) synthetic population improved through recurrent selection via shuttle breeding in two sites. To this end, we used genomic prediction on 334 S0 genotypes evaluated with early generation progeny testing (S0:2 and S0:3) across two sites. Four traits were measured (plant height, days to flowering, grain yield, and grain zinc concentration) and the predictive ability was assessed for the target site. For days to flowering and plant height, which correlate well among sites (0.51-0.62), an increase of up to 0.4 in predictive ability was observed when the model was trained using the two sites. For grain zinc concentration, adding the phenotype of the predicted lines in the nontarget site to the model improved the predictive ability (0.51 with two-site and 0.31 with single-site model), whereas for grain yield the gain was less (0.42 with two-site and 0.35 with single-site calibration). Through these results, we found a good opportunity to optimize the genomic recurrent selection scheme and maximize the use of resources by performing early progeny testing in two sites for traits with best expression and/or relevance in each specific environment.
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Affiliation(s)
- Cédric Baertschi
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
| | - Tuong-Vi Cao
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
| | - Jérôme Bartholomé
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
- Rice Breeding Platform, International Rice Research Institute, Metro Manila, Philippines
| | - Yolima Ospina
- Alliance Bioversity-CIAT, Recta Palmira Cali, Colombia
| | | | - Julien Frouin
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
| | - Jean-Marc Bouvet
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
- CIRAD, Dispositif de Recherche et d’Enseignement en Partenariat “Forêts et Biodiversité à Madagascar”, Antananarivo, Madagascar
| | - Cécile Grenier
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
- Alliance Bioversity-CIAT, Recta Palmira Cali, Colombia
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Tanaka R, Lui-King J, Mandaharisoa ST, Rakotondramanana M, Ranaivo HN, Pariasca-Tanaka J, Kanegae HK, Iwata H, Wissuwa M. From gene banks to farmer's fields: using genomic selection to identify donors for a breeding program in rice to close the yield gap on smallholder farms. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:3397-3410. [PMID: 34264372 PMCID: PMC8440315 DOI: 10.1007/s00122-021-03909-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 07/06/2021] [Indexed: 06/13/2023]
Abstract
KEY MESSAGE Despite phenotyping the training set under unfavorable conditions on smallholder farms in Madagascar, we were able to successfully apply genomic prediction to select donors among gene bank accessions. Poor soil fertility and low fertilizer application rates are main reasons for the large yield gap observed for rice produced in sub-Saharan Africa. Traditional varieties that are preserved in gene banks were shown to possess traits and alleles that would improve the performance of modern variety under such low-input conditions. How to accelerate the utilization of gene bank resources in crop improvement is an unresolved question and here our objective was to test whether genomic prediction could aid in the selection of promising donors. A subset of the 3,024 sequenced accessions from the IRRI rice gene bank was phenotyped for yield and agronomic traits for two years in unfertilized farmers' fields in Madagascar, and based on these data, a genomic prediction model was developed. This model was applied to predict the performance of the entire set of 3024 accessions, and the top predicted performers were sent to Madagascar for confirmatory trials. The prediction accuracies ranged from 0.10 to 0.30 for grain yield, from 0.25 to 0.63 for straw biomass, to 0.71 for heading date. Two accessions have subsequently been utilized as donors in rice breeding programs in Madagascar. Despite having conducted phenotypic evaluations under challenging conditions on smallholder farms, our results are encouraging as the prediction accuracy realized in on-farm experiments was in the range of accuracies achieved in on-station studies. Thus, we could provide clear empirical evidence on the value of genomic selection in identifying suitable genetic resources for crop improvement, if genotypic data are available.
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Affiliation(s)
- Ryokei Tanaka
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo, 113-8657, Japan
| | - James Lui-King
- International Program in Agricultural Development Studies, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo, 113-8657, Japan
- Crop, Livestock and Environment Division, Japan International Research Center for Agricultural Sciences (JIRCAS), 1-1 Ohwashi, Tsukuba, Ibaraki, 305-8686, Japan
| | - Sarah Tojo Mandaharisoa
- Rice Research Department, The National Center for Applied Research On Rural Development (FOFIFA), Antananarivo, 101, Madagascar
| | - Mbolatantely Rakotondramanana
- Rice Research Department, The National Center for Applied Research On Rural Development (FOFIFA), Antananarivo, 101, Madagascar
| | - Harisoa Nicole Ranaivo
- Rice Research Department, The National Center for Applied Research On Rural Development (FOFIFA), Antananarivo, 101, Madagascar
| | - Juan Pariasca-Tanaka
- Crop, Livestock and Environment Division, Japan International Research Center for Agricultural Sciences (JIRCAS), 1-1 Ohwashi, Tsukuba, Ibaraki, 305-8686, Japan
| | - Hiromi Kajiya Kanegae
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo, 113-8657, Japan
| | - Hiroyoshi Iwata
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo, 113-8657, Japan
| | - Matthias Wissuwa
- Crop, Livestock and Environment Division, Japan International Research Center for Agricultural Sciences (JIRCAS), 1-1 Ohwashi, Tsukuba, Ibaraki, 305-8686, Japan.
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10
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Baseggio M, Murray M, Wu D, Ziegler G, Kaczmar N, Chamness J, Hamilton JP, Buell CR, Vatamaniuk OK, Buckler ES, Smith ME, Baxter I, Tracy WF, Gore MA. Genome-wide association study suggests an independent genetic basis of zinc and cadmium concentrations in fresh sweet corn kernels. G3 (BETHESDA, MD.) 2021; 11:6287658. [PMID: 34849806 PMCID: PMC8496296 DOI: 10.1093/g3journal/jkab186] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 05/25/2021] [Indexed: 01/05/2023]
Abstract
Despite being one of the most consumed vegetables in the United States, the elemental profile of sweet corn (Zea mays L.) is limited in its dietary contributions. To address this through genetic improvement, a genome-wide association study was conducted for the concentrations of 15 elements in fresh kernels of a sweet corn association panel. In concordance with mapping results from mature maize kernels, we detected a probable pleiotropic association of zinc and iron concentrations with nicotianamine synthase5 (nas5), which purportedly encodes an enzyme involved in synthesis of the metal chelator nicotianamine. In addition, a pervasive association signal was identified for cadmium concentration within a recombination suppressed region on chromosome 2. The likely causal gene underlying this signal was heavy metal ATPase3 (hma3), whose counterpart in rice, OsHMA3, mediates vacuolar sequestration of cadmium and zinc in roots, whereby regulating zinc homeostasis and cadmium accumulation in grains. In our association panel, hma3 associated with cadmium but not zinc accumulation in fresh kernels. This finding implies that selection for low cadmium will not affect zinc levels in fresh kernels. Although less resolved association signals were detected for boron, nickel, and calcium, all 15 elements were shown to have moderate predictive abilities via whole-genome prediction. Collectively, these results help enhance our genomics-assisted breeding efforts centered on improving the elemental profile of fresh sweet corn kernels.
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Affiliation(s)
- Matheus Baseggio
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Matthew Murray
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Di Wu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Gregory Ziegler
- Donald Danforth Plant Science Center, St. Louis, MO 63132, USA
| | - Nicholas Kaczmar
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - James Chamness
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - John P Hamilton
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
| | - C Robin Buell
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Olena K Vatamaniuk
- Soil and Crop Sciences Section, Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Edward S Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.,Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA.,US Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, NY 14853, USA
| | - Margaret E Smith
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Ivan Baxter
- Donald Danforth Plant Science Center, St. Louis, MO 63132, USA
| | - William F Tracy
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
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11
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Wu D, Tanaka R, Li X, Ramstein GP, Cu S, Hamilton JP, Buell CR, Stangoulis J, Rocheford T, Gore MA. High-resolution genome-wide association study pinpoints metal transporter and chelator genes involved in the genetic control of element levels in maize grain. G3-GENES GENOMES GENETICS 2021; 11:6156830. [PMID: 33677522 PMCID: PMC8759812 DOI: 10.1093/g3journal/jkab059] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 02/21/2021] [Indexed: 12/18/2022]
Abstract
Despite its importance to plant function and human health, the genetics underpinning element levels in maize grain remain largely unknown. Through a genome-wide association study in the maize Ames panel of nearly 2,000 inbred lines that was imputed with ∼7.7 million SNP markers, we investigated the genetic basis of natural variation for the concentration of 11 elements in grain. Novel associations were detected for the metal transporter genes rte2 (rotten ear2) and irt1 (iron-regulated transporter1) with boron and nickel, respectively. We also further resolved loci that were previously found to be associated with one or more of five elements (copper, iron, manganese, molybdenum, and/or zinc), with two metal chelator and five metal transporter candidate causal genes identified. The nas5 (nicotianamine synthase5) gene involved in the synthesis of nicotianamine, a metal chelator, was found associated with both zinc and iron and suggests a common genetic basis controlling the accumulation of these two metals in the grain. Furthermore, moderate predictive abilities were obtained for the 11 elemental grain phenotypes with two whole-genome prediction models: Bayesian Ridge Regression (0.33–0.51) and BayesB (0.33–0.53). Of the two models, BayesB, with its greater emphasis on large-effect loci, showed ∼4–10% higher predictive abilities for nickel, molybdenum, and copper. Altogether, our findings contribute to an improved genotype-phenotype map for grain element accumulation in maize.
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Affiliation(s)
- Di Wu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Ryokei Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Xiaowei Li
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | | | - Suong Cu
- College of Science and Engineering, Flinders University, Bedford Park, SA 5042, Australia
| | - John P Hamilton
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
| | - C Robin Buell
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
| | - James Stangoulis
- College of Science and Engineering, Flinders University, Bedford Park, SA 5042, Australia
| | - Torbert Rocheford
- Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
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12
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Zhu M, Tong L, Xu M, Zhong T. Genetic dissection of maize disease resistance and its applications in molecular breeding. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:32. [PMID: 37309327 PMCID: PMC10236108 DOI: 10.1007/s11032-021-01219-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 02/25/2021] [Indexed: 06/14/2023]
Abstract
Disease resistance is essential for reliable maize production. In a long-term tug-of-war between maize and its pathogenic microbes, naturally occurring resistance genes gradually accumulate and play a key role in protecting maize from various destructive diseases. Recently, significant progress has been made in deciphering the genetic basis of disease resistance in maize. Enhancing disease resistance can now be explored at the molecular level, from marker-assisted selection to genomic selection, transgenesis technique, and genome editing. In view of the continuing accumulation of cloned resistance genes and in-depth understanding of their resistance mechanisms, coupled with rapid progress of biotechnology, it is expected that the large-scale commercial application of molecular breeding of resistant maize varieties will soon become a reality.
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Affiliation(s)
- Mang Zhu
- State Key Laboratory of Plant Physiology and Biochemistry/College of Agronomy and Biotechnology/National Maize Improvement Center/Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, 2 West Yuanmingyuan Road, Beijing, 100193 People’s Republic of China
| | - Lixiu Tong
- State Key Laboratory of Plant Physiology and Biochemistry/College of Agronomy and Biotechnology/National Maize Improvement Center/Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, 2 West Yuanmingyuan Road, Beijing, 100193 People’s Republic of China
| | - Mingliang Xu
- State Key Laboratory of Plant Physiology and Biochemistry/College of Agronomy and Biotechnology/National Maize Improvement Center/Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, 2 West Yuanmingyuan Road, Beijing, 100193 People’s Republic of China
| | - Tao Zhong
- State Key Laboratory of Plant Physiology and Biochemistry/College of Agronomy and Biotechnology/National Maize Improvement Center/Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, 2 West Yuanmingyuan Road, Beijing, 100193 People’s Republic of China
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